Hello everyone. I'm myself Esta, and I have two 12 plus years of experience in the field of DevOps and automation middleware technologies. Today we are going to explore one of the most exciting and transformative shifts happening in all of cybersecurity and cloud engineering. We are entering an era where security is no longer a bottleneck. A final checkpoint or a reactive process instore. It becomes predictive, autonomous, continuously learning, and deeply embedded in the entire lifecycle of software delivery. AI is reshaping DevSecOps in ways we could not have imagined even a few years ago. What once required? Massive manual effort. Long tricycles and human interpretation now can be handled automatically, often in seconds. What previously slowed teams down can now enable faster innovation with stronger production, and what used to LMAs with noise can now be distal into meaningful contextual intelligence. My goal today is to walk you through this transformation slide by slide, and show how AI changes not only tools and process. Mindsets, cultures and operating models. Okay, DevSecOps Revolution. We begin with the core idea of this presentation. AI powered autonomous cloud security. The future of DevSecOps is not just about automating tasks. It's about intelligent systems that can anticipate, interpret, and respond to threats in real time. Traditionally, security was bolted on at the end. Today we integrate AI driven security into every stage, like design, coding, infrastructure, provisioning, deployment, and operations. Moving on the security velocity paradox, one of the oldest distinctions in software engineering is the battle between speed and security. Teams want to deploy fast release features quickly and keep up with business demands. Security teams want. Breaker, precision and deep analysis. This mismatch leads to friction manual gates, slow developers scanners, oral security teams with thousands of alerts. Compliance introduces delays that can stall entire release, but with threats evolving faster than ever. We cannot sacrifice speed or security. We need both. And AI finally allows us to achieve that balance the AI solution. AI resolves this paradox by changing the nature of security work, rather than reacting to vulnerabilities after they appear, AI predicts them instead of waiting for reports. AI analyzes continuously. Instead of relying on humans to shift through false positives, AI triages, prioritizes and even fixes issues automatically. AI is in just a faster scanner. It's an intelligent partner that enables developers to ship faster and safer. Moving on beyond automation. This revolution goes far beyond basic automation. Mission learning recognizes subtle patterns that even experienced analysts may miss. Neural networks understand relationships across cloud resources and traditional tools. Analyze in isolation. Reinforcement learning improves decision making with every iteration. Learning from the environment just like a human would. We are shifting from procedural automation to adaptive intelligence. Moving on to Evolution of DevSecOps, let's take a broader look at how we got here. Legacy security. Security occurred at end, painful, slow, and was a bit risky early DevSecOps. Shift left, improved timing, but still rely heavily on manual work tool proliferation. Tools multiplied, but context didn't. Teams were buried in alerts, AI revolution. Now we use intelligence prediction and automation to reduce noise and increase accuracy. This evaluation is shaping the next decade of software engineering. Core AI technologies mission learning moral strain on millions of core samples can spot vulnerabilities never seen before. Neural networks analyze cloud configurations holistically, not individually. Reinforcement learning finds the most secure and these relationships attackers don't. Compromise one resource. They chain misconfigurations to move laterally. GNNS can detect these multi-step attack parts in ways that humans simply cannot scale to. This is the next frontier of cloud security intelligence. Architectural implementation. A critical factor in successful adoption is seamless integration. AI must be unusable to users working inside the IDE scanning IAE during pull requests, validating deployments in real time technologies like federated learning model versioning and continuous feedback cycles ensure these systems evolve without causing disruption. Okay. One of the most powerful outcomes of AI adoption is automated vulnerability remediation. Traditional workflows involve security scanning, ticket creation, developer rework, reviews, and retesting a cycle that can take weeks. AI shortens this to minutes by fixing common vulnerabilities automatically. While preserving the controls collectively, if each setting looks correct individually, AI can detect dangerous interactions between them. Many organizations have discovered issues they were unaware of for years thanks to AI analysis. Moving on to predictive security. This slide actually captures the heart of modern cybersecurity prediction, AI analysis, behavior patterns, commit histories, deployment trends and infrastructure changes. To anticipate where vulnerabilities are likely to occur. This moves us from firefighting to foresight, dramatically improving resilience. Continuous compliance. Compliance has historically been slow, and this makes regulated industries more efficient and significantly reduces risk. AI decisions must be explainable. We address these with adversarial training, differential privacy, federator learning, and model explainability frameworks like lime and sharp. Responsible AI is essential. New skills and team structures. Security engineers must understand ML basics. ML engineers must understand security. Cross-functional collaboration becomes the default operating model. Importantly, we frame AI as an augmentation tool, not a threat to jobs strategic implementation roadmap. AI transformation isn't just about technology. It requires new skills and team structures, strategic implementation roadmap. To adopt AI in DevSecOps, organizations must access maturity. Start with high value pilots decide on build versus buy measure outcomes. Scale successful initiatives. AI is long-term journey that requires iteration and adaptability. Autonomous security operations. The future is autonomous AI agents will investigate threats, generate policies, analyze cloud, posture, and handle incident response. Humans will oversee strategy, ethics, and complex decision making. This is where the industry is going, and organizations that prepare now will lead the future. Thank you for your time. AI driven SecOps is not just an exciting idea. It's a necessity for modern organizations. It improves speed, accuracy, resilience, and overall security posture. As we conclude, I want to leave with this thought. Security is no longer about reacting to the last thread. It's about predicting the next one. AI gives us the ability to stay ahead. To build systems that learn, heal, and adapt. The future of DevSecOps is autonomous, intelligent, and resilient, and the organizations that embrace this future today will be the leaders of tomorrow. Thank you once again.